Augmented reality overlay based on self-driving mode

ABSTRACT

A computer-implemented method displays a media file below a distraction threshold. The method includes identifying a driver of a vehicle, where the vehicle includes an augmented reality system, and the vehicle is at least partially automated. The method also includes determining, a driving mode for the vehicle and calculating a focus score. The method includes requesting, from a media repository, a media file, where the media repository contains a plurality of media files, and each media file has an associated distractions score. The method includes receiving from the media repository, a first media file to display, where a first distraction score of the first media file combined with the focus score is below a safety threshold. The method also includes displaying the first media file by the AR system. Further aspects of the present disclosure are directed to computer program products containing functionality consistent with the method described above.

BACKGROUND

The present disclosure relates to automated vehicles, and, morespecifically, altering augmented reality overlays based on self-drivingmode.

Many modern vehicles have several modes/levels of automation. These canrange from no automation or fully manual where a driver performs alltasks related to driving, through partially automated, where the driverperforms some of the tasks, to fully automated, where the driverperforms almost no tasks related to driving.

SUMMARY

Disclosed is a computer-implemented method to display a media file belowa distraction threshold. The method includes identifying a driver of avehicle, wherein the vehicle includes an augmented reality (AR) system,and the vehicle is at least partially automated. The method alsoincludes determining, a driving mode for the vehicle. The method furtherincludes calculating a focus score, wherein the focus score is based onthe driving mode. The method includes requesting, from a mediarepository, a media file, wherein the media repository contains aplurality of media files, and each media file has an associateddistractions score. The method further includes receiving from the mediarepository, a first media file to display, wherein a first distractionscore of the first media file combined with the focus score is below asafety threshold. The method also includes displaying the first mediafile by the AR system. Further aspects of the present disclosure aredirected to computer program products containing functionalityconsistent with the method described above. Also disclosed is a systemto reduce the risk of distractions to a driver.

The system comprises a processor; and a computer-readable storage mediumcommunicatively coupled to the processor and storing programinstructions which, when executed by the processor, are configured tocause the processor to identify a driver of a vehicle, wherein thevehicle includes an augmented reality (AR) system, and the vehicle is atleast partially automated. The program instructions are furtherconfigured to cause the processer to determine a driving mode for thevehicle. The program instructions are also configured to cause theprocesser to calculate a focus score, wherein the focus score is basedon the driving mode. The program instructions are configured to causethe processer to identify a first distraction along a route the vehicleis traveling. The program instructions are further configured to causethe processer to calculate a distraction score for the firstdistraction, wherein the distraction score is based on a complexity ofthe distraction. The program instructions are also configured to causethe processer to determine the sum of the focus score and thedistraction score exceed a safety threshold. The program instructionsare further configured to cause the processer to reduce a risk of thefirst distraction.

The present Summary is not intended to illustrate each aspect of, everyimplementation of, and/or every embodiment of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are described herein with reference to differentsubject-matter. In particular, some embodiments may be described withreference to methods, whereas other embodiments may be described withreference to apparatuses and systems. However, a person skilled in theart will gather from the above and the following description that,unless otherwise notified, in addition to any combination of featuresbelonging to one type of subject-matter, also any combination betweenfeatures relating to different subject-matter, in particular, betweenfeatures of the methods, and features of the apparatuses and systems,are considered as to be disclosed within this document.

The aspects defined above, and further aspects disclosed herein, areapparent from the examples of one or more embodiments to be describedhereinafter and are explained with reference to the examples of the oneor more embodiments, but to which the invention is not limited. Variousembodiments are described, by way of example only, and with reference tothe following drawings:

FIG. 1 depicts a cloud computing environment, according to an embodimentof the present invention.

FIG. 2 depicts abstraction model layers, according to an embodiment ofthe present invention.

FIG. 3 is a block diagram of a data processing system (DPS), accordingto one or more embodiments disclosed herein.

FIG. 4 illustrates a functional diagram of a computing environmentsuitable for operation of a display manager, in accordance with someembodiments of the present disclosure.

FIG. 5 illustrates a flow chart of an example method to obscure adistraction object while driving a vehicle, in accordance with someembodiments of the present disclosure.

FIG. 6 illustrates a flow chart of an example method to display a mediafile below a determined distraction level, in accordance with someembodiments of the present disclosure.

DETAILED DESCRIPTION

The present disclosure relates to automated vehicles, and, morespecifically, altering augmented reality overlays based on aself-driving mode.

Many modern vehicles have several modes/levels of automation. These canrange from no automation or fully manual where a driver performs alltasks related to driving, to fully automated, where the driver performsalmost no tasks related to driving.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics Are as Follows

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice’s provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, andpersonal digital assistants (PDAs)).

Resource pooling: the provider’s computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models Are as Follows

Software as a Service (SaaS): the capability provided to the consumer isto use the provider’s applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models Are as Follows

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 1 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 1 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 2 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and risk reduction 96.

Data Processing System in General

FIG. 3 is a block diagram of an example data processing system (DPS)according to one or more embodiments. The DPS may be used as a cloudcomputing node 10. In this illustrative example, the DPS 100 may includecommunications bus 102, which may provide communications between aprocessor unit 104, a memory 106, persistent storage 108, acommunications unit 110, an Input/Output (I/O) unit 112, and a display114.

The processor unit 104 serves to execute instructions for software thatmay be loaded into the memory 106. The processor unit 104 may be anumber of processors, a multi-core processor, or some other type ofprocessor, depending on the particular implementation. A number, as usedherein with reference to an item, means one or more items. Further, theprocessor unit 104 may be implemented using a number of heterogeneousprocessor systems in which a main processor is present with secondaryprocessors on a single chip. As another illustrative example, theprocessor unit 104 may be a symmetric multi-processor system containingmultiple processors of the same type.

The memory 106 and persistent storage 108 are examples of storagedevices 116. A storage device may be any piece of hardware that iscapable of storing information, such as, for example without limitation,data, program code in functional form, and/or other suitable informationeither on a temporary basis and/or a permanent basis. The memory 106, inthese examples, may be, for example, a random access memory or any othersuitable volatile or non-volatile storage device. The persistent storage108 may take various forms depending on the particular implementation.

For example, the persistent storage 108 may contain one or morecomponents or devices. For example, the persistent storage 108 may be ahard drive, a flash memory, a rewritable optical disk, a rewritablemagnetic tape, or some combination of the above. The media used by thepersistent storage 108 also may be removable. For example, a removablehard drive may be used for the persistent storage 108.

The communications unit 110 in these examples may provide forcommunications with other DPSs or devices. In these examples, thecommunications unit 110 is a network interface card. The communicationsunit 110 may provide communications through the use of either or bothphysical and wireless communications links.

The input/output unit 112 may allow for input and output of data withother devices that may be connected to the DPS 100. For example, theinput/output unit 112 may provide a connection for user input through akeyboard, a mouse, and/or some other suitable input device. Further, theinput/output unit 112 may send output to a printer. The display 114 mayprovide a mechanism to display information to a user.

Instructions for the operating system, applications and/or programs maybe located in the storage devices 116, which are in communication withthe processor unit 104 through the communications bus 102. In theseillustrative examples, the instructions are in a functional form on thepersistent storage 108. These instructions may be loaded into the memory106 for execution by the processor unit 104. The processes of thedifferent embodiments may be performed by the processor unit 104 usingcomputer implemented instructions, which may be located in a memory,such as the memory 106.

These instructions are referred to as program code, computer usableprogram code, or computer readable program code that may be read andexecuted by a processor in the processor unit 104. The program code inthe different embodiments may be embodied on different physical ortangible computer readable media, such as the memory 106 or thepersistent storage 108.

The program code 118 may be located in a functional form on the computerreadable media 120 that is selectively removable and may be loaded ontoor transferred to the DPS 100 for execution by the processor unit 104.The program code 118 and computer readable media 120 may form a computerprogram product 122 in these examples. In one example, the computerreadable media 120 may be computer readable storage media 124 orcomputer readable signal media 126. Computer readable storage media 124may include, for example, an optical or magnetic disk that is insertedor placed into a drive or other device that is part of the persistentstorage 108 for transfer onto a storage device, such as a hard drive,that is part of the persistent storage 108. The computer readablestorage media 124 also may take the form of a persistent storage, suchas a hard drive, a thumb drive, or a flash memory, that is connected tothe DPS 100. In some instances, the computer readable storage media 124may not be removable from the DPS 100.

Alternatively, the program code 118 may be transferred to the DPS 100using the computer readable signal media 126. The computer readablesignal media 126 may be, for example, a propagated data signalcontaining the program code 118. For example, the computer readablesignal media 126 may be an electromagnetic signal, an optical signal,and/or any other suitable type of signal. These signals may betransmitted over communications links, such as wireless communicationslinks, optical fiber cable, coaxial cable, a wire, and/or any othersuitable type of communications link. In other words, the communicationslink and/or the connection may be physical or wireless in theillustrative examples.

In some illustrative embodiments, the program code 118 may be downloadedover a network to the persistent storage 108 from another device or DPSthrough the computer readable signal media 126 for use within the DPS100. For instance, program code stored in a computer readable storagemedium in a server DPS may be downloaded over a network from the serverto the DPS 100. The DPS providing the program code 118 may be a servercomputer, a client computer, or some other device capable of storing andtransmitting the program code 118.

The different components illustrated for the DPS 100 are not meant toprovide architectural limitations to the manner in which differentembodiments may be implemented. The different illustrative embodimentsmay be implemented in a DPS including components in addition to or inplace of those illustrated for the DPS 100. Other components shown inFIG. 1 .

The present disclosure relates to automated vehicles, and, morespecifically, altering augmented reality overlays based on self-drivingmode.

Many modern vehicles have several modes/levels of automation. These canrange from no automation or fully manual where a driver performs alltasks related to driving, to partially automated, where the driverperforms some of the tasks, to fully automated, where the driverperforms almost no tasks related to driving. Many vehicles can switchbetween the various driving modes automatically and/or based actions ofthe driver. The amount of attention the driver needs to focus on drivingcan be different for the different levels of automation. Many vehiclesinclude augmented reality (AR) features that are incorporated into thevehicles. The AR displays information and/or projects information tovarious compatible surfaces within the vehicle. Augmented reality can beany system that enhances/alters a user’s perception on theirsurroundings. For example, a vehicle can include a Heads-Up Display(HUD), that can display driving directions within the field of view ofthe driver, so a driver does not need to look away from the road togather the relevant information.

When a vehicle is in manual mode, the risk of a driver being distractedfrom driving can increase the risk of a negative event (e.g., caraccident). However, the risk can be much lower relative to the samedistraction when a vehicle is in a highly automized mode. For example,if a driver is driving a vehicle in manual mode and the driver looks atan advertisement for a long period of time, they are taking theirconcentration off the road and increasing the risk of negative event.Conversely, if the vehicle is driven in fully autonomous mode, thedriver can look at the same roadside advertisement for the same amountof time with little not no increase in risk. Examples of a distractionscan include roadside advertisements, displays, and/or otherimages/activities.

Another factor that can affect distraction/safety is the experiencelevel of the driver. A driver with relatively low experience can bedistracted, or be more likely to have a negative outcome during a smalldistraction, than a more experienced driver.

Embodiments of the present disclosure can limit the distractionspresented to a driver, or said differently, to reduce the distractionbelow an acceptable level based on the automation state (e.g., manualcontrol vs. automatic control) of a self-driving vehicle.

In some embodiments, a display manager can manager what images arepresented to a driver. The display manager can cause an image (e.g.,advertisement) to be displayed and/or use AR capabilities to block/blura distraction from outside the vehicle.

In some embodiments, the display manager can identify a driver. Theidentification can be based on receiving an indication from the driver.In some embodiments, the identification can be based on an Internet ofThings device associated with the driver. For example, a smart phone, orsmart watch can indicate the driver in the vehicle. In some embodiments,identifying the driver include determining a driver experience level (orexperience level). The experience level can indicate a relative amountof driving experience of a driver. In some embodiments, the experiencelevel can be dynamic and/or static for each driver. The dynamicexperience level can change based on factors that can affect/needadditional concentration. Some factors can include location, weather,additional passengers, familiarity with the vehicle and location, andthe like.

In some embodiments, the experience level is determined based onhistorical data. The historical data can include data about a driver’sdriving history. The historical data can be manually input and/or addedto after and during each trip. The historical data can include timespend driving, type of vehicles, locations, routes, weather conditions,passengers, time of day, driving conditions (e.g., traffic), and thelike.

In some embodiments, the display manager can determine the driving mode(or automation mode) of the vehicle. In some embodiments, the drivingmode can be selected from a predetermined set of modes. There can be anynumber of predefined modes, where each mode has a set oftasks/operations the vehicle performs without the driver. In someembodiments, the driving mode can be based on the feature that arecurrently being used.

In some embodiments, the display manager can generate a focus score (orfocus level score, or attention score, or allowed level of distractionscore). The focus score can represent a relative amount offocus/attention a driver should use to safely drive the vehicle. Thefocus score can be based on one or more of the driving conditions, thevehicle driving mode, and the driver. As the amount of focus needed bythe driver increases, the focus score also increases.

In some embodiments, the display manager can identify potentialdistractions. The distraction can be anything that will shift the focusof the driver away from driving to the object. In some embodiments, thedistraction is a display on an interface. The display can be anadvertisement, media display (e.g., music, driving directions,automobile information, etc.), and the like. In some embodiments, thedisplay can be external to the vehicle (e.g., billboard). In someembodiments, the display can be inside the vehicle.

In some embodiments, the display manager can generate a distractionscore. The distraction score can represent an estimated attention and/orthe driver needs to give the display to internalize the information. Themore attention and/or time needed, the higher the distraction score. Asexamples, a billboard with well-known simple logo can have a lowdistraction score, but a billboard with a lot of words and graphics canhave a relatively high distraction score.

In some embodiments, the display manager can combine/add the focus scoreand the distraction score. The display manager can attempt to keep thecombination of the scores below a threshold. The threshold can representa total amount of concentration of a driver and, if the total isexceeded, the display manager can take action. The display manager canattempt to obscure the distraction and/or alter the driving mode of thevehicle. Altering the driving mode to a higher state of automation canlower the focus score. As such, the combination can be below thethreshold.

In some embodiments, the identified distraction can be obscured by thedisplay manager. The display manager can generate a display on a windowand/or other surface to obscure a potential distraction. For example, ifthe distraction is on a billboard, the AR device can project a bluesquare to mimic the sky over the location of the sign. The projectioncan be of any shape and/or color configured to obscure the distraction.In some embodiments, the display is within the vehicle. The display canbe simplified, and/or turned off to reduce the potential distraction. Insome embodiments, the vehicle includes one more sensors to identify thepotential distractions. The sensors can include cameras, head detectors,IoT devices, and the like. The sensors can be in the vehicle to capturedata related to the drives (e.g., where the eyes are looking), and/oroutside the vehicle to observe distractions, traffic, weather, and/orother conditions surrounding the vehicle. In some embodiments, thepotential distraction is based on a trigger. The trigger can be, forexample, a location (e.g., passing a billboard), amount of traffic,weather conditions, and the like.

In some embodiments, the display manager can select anadvertisement/distraction to display. The display manager can be incommunication with an advertising server/application. The applicationcan have a set of adds for a plurality of distraction levels. In someembodiments, the selected advertisement has a potential distraction suchthat, when combined with the focus score, the threshold is not exceeded.In some embodiments, the advertisement is selected in response to thetrigger being activated.

The aforementioned advantages are example advantages, and embodimentsexist that can contain all, some, or none of the aforementionedadvantages while remaining within the spirit and scope of the presentdisclosure.

Referring now to various embodiments of the disclosure in more detail,FIG. 4 is a representation of a computing environment 400, that iscapable of running a display manager in accordance with one or moreembodiments of the present disclosure. Many modifications to thedepicted environment may be made by those skilled in the art withoutdeparting from the scope of the disclosure.

Computing environment 400 includes host 410, vehicle 420, and network440. Network 440 can be, for example, a telecommunications network, alocal area network (LAN), a wide area network (WAN), such as theInternet, or a combination of the three, and can include wired,wireless, or fiber optic connections. Network 440 may include one ormore wired and/or wireless networks that are capable of receiving andtransmitting data, voice, and/or video signals, including multimediasignals that include voice, data, and video information. In general,network 440 may be any combination of connections and protocols thatwill support communications between and among host 410, vehicle 420, andother computing devices (not shown) within computing environment 400. Insome embodiments, host 410 and vehicle 420 may include a computersystem, such as the data processing system 100 of FIG. 3 .

Host 410 can be a standalone computing device, a management server, aweb server, a mobile computing device, or any other electronic device orcomputing system capable of receiving, sending, and processing data. Inother embodiments, host 410 can represent a server computing systemutilizing multiple computers as a server system, such as in a cloudcomputing environment (e.g., cloud environment 50). In some embodiments,host 410 includes advertisements 412.

In some embodiments, host 410 and/or any number of its subcomponents canbe implemented into vehicle 420.

Advertisements 412 can include a plurality media files. In someembodiments, the files can include advertisements and/or other visualmedia (e.g., photos, images, etc.). The files can include still frames,videos, audio, and other similar media files. In some embodiments, eachmedia file is associated with a complexity score and/or a distractionscore. The two scores can form a single combined score. In someembodiments, the distraction score can represent the complexity of themedia display. The higher the score, the more complex the media. In someembodiments, more complex media can require more attention to viewand/or understand. For example, a video can be more complex than a stillframe because it require viewing for a longer period of time. Anotherexample, a display that only has a logo can be less complex than adisplay that has the same logo with words and/or numbers associated withthe logo.

Vehicle 420 can be any vehicle. In some embodiments, vehicle 420 can beany machine that is propelled by an engine. In some embodiments,vehicle, 420 can be anything that is driven around on the ground (e.g.,wheeled or unwheeled). The vehicle can be an automobile, a bicycle, anall-terrain vehicle, farm equipment, and any other type of movablemachine. In some embodiments, vehicle 420 has some level ofautomation/self-driving capability. Each vehicle can include a computingdevice to manage the automation. In some embodiments, vehicle 420includes a driving application 422, AR interface 424, sensors 426, anddisplay manager 430.

Driving application 422 can be any combination of hardware and/orsoftware configured to automate at least a portion of vehicle 420. Theautomation can be any level of automation. In some embodiments, drivingapplication 422 can automate one or more of accelerating, decelerating,maintaining velocity, maintaining distance, steering, changing lanes,parking, and the like. In some embodiments, the driving application canmanage an automated function in the vehicle unrelated to driving. Thiscan include turning on/off attached equipment, passenger comfortactions, communications, and the like.

In some embodiments, the automobile is fully automated. When fullyautomated, driving application 422 performs all functions to drive thevehicle such that a passenger/driver takes no actions and vehicle 422continues to drive without user input. In some embodiments, the level ofautomation is very low, such that driving application 422 can perform asingle action. For example, the automation can maintain an engine at aconstant revolutions per minute (RPM) and/or a constant velocity. Insome embodiments, the level of automation can be any level. Any functionof driving can be performed by driving application 422.

In some embodiments, driving application 422 can have preset automationlevels/modes. Each level can have a set of functions that are performedby the driving application 422. The various functions are notnecessarily mutually exclusive and/or additive. In some embodiments,each level builds on the next lower level by adding at least oneadditional function to all of the functions of the lower levels. Therecan be any number of predefined levels. In some embodiments, the higherthe level of automation the lower the focus score all other factorsbeing equal.

In some embodiments, the driving mode and/or level of automation can becorrelated with a focus level. For example, the focus score isdetermined in full by the current driving mode of the vehicle. In someembodiments, the driving mode is one factor in determining the focusscore. In some embodiments, the driving mode can set a minimum focus.For example, in driving mode A, the focus score must be X or higher.Factors such as weather and driver experience can cause the focus scoreto increase, but not to decrease.

AR interface 424 can be any combination of hardware and/or softwareconfigured to display visual information to the driver and otherpassengers of vehicle 420. In some embodiments, AR interface 424 can usevisual, audible, and/or other computer sensory generated data to enhanceperception. AR interface 424 can be overlayed on any surface (e.g.,windshield, display screen, etc.) within vehicle 420. In someembodiments, AR interface 424 can accept input from one or morepassengers within vehicle 420. In some embodiments, AR interface 424 canproject/render advertisements within vehicle 420.

Sensors 426 can be any combination of hardware and/or softwareconfigured to gather data surrounding a vehicle 420. Vehicle 420 caninclude any number sensors and any number of types of sensors. In someembodiments, the type of sensor includes one or more of cameras,thermometers, heat detectors, moisture detectors, motion detections,microphones, distance detectors, and the like. The number and type ofsensors can be configured to gather enough data to identify objectand/or situations that can affect driving a vehicle. In someembodiments, the various sensors can be placed in various locationsaround the vehicle. In some embodiments, sensors 426 can be configuredto identify visual displays, such as advertisements. In someembodiments, sensors 426 can be configured to identify conditionssurrounding vehicle 420. The conditions can include weather conditions,traffic conditions, and the like.

Display manager 430 can be any combination of hardware and/or softwareconfigured to manage content that is displayed to a driver. In someembodiments, display manager 430 can display media to, and/orremove/obscure media from the field of view of a driver. In someembodiments, display manager 430 includes driver profile 432 and/orhistorical data 434.

In some embodiments, display manager 430 can generate a focus score. Thefocus score can be a representation of the amount of attention thedriver of the vehicle should use to drive. In general, the higher thelevel of automation, the lower the focus score. In some embodiments, thefocus score can be based on a driving mode of the vehicle, environmentalfactors, driver experience, traffic, and the like.

In some embodiments, display manager 430 can identify a potentialdistraction to the driver. In some embodiments, the potentialdistraction includes a visual distraction. The visual distraction caninclude a billboard, and/or other similar items. Display manager 430 cangenerate a distraction score for the potential distraction. Thedistraction score can be based on the complexity of the visual display,the location, driver interest, and the like. For example, if there is abillboard in line of sight with the road, that can be a low distraction,because the driver can still be viewing the road. If the billboard isoff to the side, the distraction score can be relatively high because itwould require the driver to move attention away from the road.

In some embodiments, display manger 430 can determine if the distractionlevel will exceed a threshold based on the focus score. In someembodiments, display manager 430 can compare the focus score against thedistraction score. In some embodiments, the comparing is in response toidentifying the distraction and/or generating the distraction score. Insome embodiments, display manager 430 can combine the distraction scorewith a focus score. The two scores together can be compared to athreshold. The threshold can be a safety threshold. In some embodiments,display manager 430 can take action based on the safety threshold beingexceeded by the combination of the scores.

In some embodiments, display manager 430 can obscure or attempt toobscure the distraction. The obscuring can be in response to determiningthe distraction score is greater than the focus score. In someembodiments, the obscuring can include turning off and/or changing adisplay. For example, if the distraction is located on a screen withinvehicle 420, display manager 430 can turn the screen off and/or changeto a display with a lower distraction score. In some embodiments, theobscuring includes covering the distraction with AR interface 424. ARinterface 424 can project/render on a window any display to obscure aview. For example, if the distraction is a billboard on the drives side,a portion of the driver’s side window can be obscured with a coloredbox. The box can be configured to mimic the background, such as blue boxto look like the sky behind the billboard.

In some embodiments, display manager 430 can be configured to displaymedia to the driver based on one or more triggers. The trigger can be alocation (e.g., GPS), a time, distance travelled, and the like. In someembodiments, display manager 430 can generate the focus score inresponse to the trigger. In some embodiments, display manager 430retrieves media to display based on the current focus score. Theretrieved media can be obtained from advertisement repository 412. Insome embodiments, the media can have a distraction score less than orequal to the focus score. In some embodiments, the media is selectedbased such that the associated distraction score does not exceed thesafety threshold when combined with the focus score. In someembodiments, the media can be an advertisement. The advertisement can beselected based on data in driver profile 432.

Driver profile 432 can include information about one or more drivers ofvehicle 420. In some embodiments, driver profile 432 can include aplurality of data for a driver. In some embodiments, driver profile 432includes two or more profiles for two or more drivers. In someembodiments, driver profile 432 is based on vehicle 420. The profile canbe shared for multiple vehicles. Each particular vehicle can have dataunique to that vehicle 420. In some embodiments, driver profile 432 caninclude a skill level for the driver.

Historical data 434 can be data related to how one or more usersinteract with vehicle 420, different vehicles, and/or various media fromadvertisement repository 412. In some embodiments, historical data 434includes driving history for each driver profile 432. The driver profile432 can use data within historical data 434 to generate and/or update askill level for the driver. In some embodiments, historical data 434 canbe used to generate the distraction score and/or the focus score. Forexample, data regarding how the driver interacted with/was distracted bya previously displayed advertisement can be used to generate a futurescore. Another example, the frequency and/or recency of a driver beingin a particular area can have an effect on the focus score.

FIG. 5 depicts a flowchart of an example method, method 500, forobscuring potential distractions based on a focus score that can beperformed in a computing environment (e.g., computing environment 400and/or cloud computing environment 50). One or more of the advantagesand improvements described above for obscuring potential distractionsmay be realized by method 500, consistent with various embodiments ofthe present disclosure.

Method 500 can be implemented by one or more processors, host 410,vehicle 420, driving application 422, AR interface 424, sensors 426,display manager 430, driver profile 432, historical data 434, and/or adifferent combination of hardware and/or software. In variousembodiments, the various operations of method 500 are performed by oneor more of host 410, vehicle 420, driving application 422, AR interface424, sensors 426, display manager 430, driver profile 432, andhistorical data 434. For illustrative purposes, the method 500 will bedescribed as being performed by display manager 430.

At operation 502, display manager 430 identifies a driver. The drivercan be a driver for a vehicle such as vehicle 420. In some embodiments,the driver is determined by receiving an input from the driver. Thedriver can select their driver profile 432 from a list of possibleprofiles. In some embodiments, the driver is determined based on one ormore sensors 426. The sensors can determine which profile the driver isassociated with. The sensors can be associated with one or more IoTdevices in vehicle 420 and/or associated with the driver.

At operation 504, display manager 430 determines a driving mode. In someembodiments, the driving mode represents an automation level of thevehicle. Said differently, the driving mode is based on what portions ofvehicle 420 are being controlled by the driver and controlled by drivingapplication 422.

In some embodiments, the determines is based on driving application 422.The driving mode can be selected based on a predetermined set of modes.In some embodiments, each mode is associated with one or more featuresthat are automated.

At operation 506, display manager 430 generates/calculates a focusscore. In some embodiments, the focus score can represent an amount ofattention the driver should put into driving. Said differently, thefocus score can represent a minimum focus required to safely operate thevehicle. The minimum focus can be correlated with a maximum distractionfactor. In some embodiments, the focus score can be represented as anumber, a vector, and/or other similar representations. In someembodiments, the focus score is a number that is less than or equal to1.

In some embodiments, the focus score is based on one or more thedetermined driving mode, driver experience level, location,environmental conditions, and the like. Each factor can have any weightin the score. In some embodiments, the driving mode is correlated tominimum focus score. If the vehicle is fully automated, then the focusscore can be 0. In some embodiments, the focus score is based on driverexperience level. For example, the less experience a driver has, thehigher the focus score. The experience can be an overall experience, anexperience related to conditions (e.g., driving in snow), and/orexperience related to a location. For example, if a driver is an areathey travel frequently, the focus score can be relatively lower than ifin a new area.

In some embodiments, the focus score can be based on environmentalconditions. Environment conditions can include weather, and/or traffic.For example, more traffic can require higher focus relative to low or notraffic. In some embodiments, the factors used in the focus score can bebased on data in driver profile 432 and/or historical data 434.

At operation 508, display manager 430 identifies a potentialdistraction. In some embodiments, the distraction can be identified bysensors 426. The distraction can be any type of media (pictures,billboards), that are in the potential view of the driver. In someembodiments, the distractions can be located at predetermined positions.For example, a map of all billboards can be incorporated into displaymanager 430. The distraction can be identified by the vehicle passingthe predetermined position.

At operation 510, display manager 430 generates/calculates a distractionscore. The distraction score can represent a prediction of how mucheffort/attention the driver will give the distraction. The distractionscore can be based on one or more of location, time of day, driverprofile (e.g., interest), previous interactions, complexity of thedistraction, and the like. As the likelihood that driver will giveattention to the distraction increases, the distraction score increases.In some embodiments, the distraction score can be based on thecomplexity. The complexity can represent how much effort is required toincorporate the distraction. As an example, assume the distraction is abillboard. If the billboard only has a well-known logo, it will have arelatively low likelihood of drawing too much attention. The meaning canbe obtained with a quick glance. If the billboard has several sentencesand images and/or is new to the driver, it can take much longer tointernalize the information. This will have a relatively highdistraction score.

In some embodiments, the distraction score can be based on driverprevious actions. For example, if the driver has been distracted by(interacted with) with a particular billboard in the past, thelikelihood of the same or similar billboard being distracting increases.This can be based on data in historical data 434 and/or driver profile432.

At operation 512 display manager 430 determines if the combination ofthe focus score and the distraction score exceed a predeterminedthreshold. The threshold can be a safety threshold. In some embodiments,display manager 430 combines the two scores together in somemathematical manner. The two score can be combined in any manner. Insome embodiments, the threshold can represent a total attention for theuser. The attention is being exceeded if the combination of scoresexceeds the threshold. For example, if there is a low focus score, thereis a potential for a larger distraction score before the threshold isexceeded. Vice versa if there is a high focus score, a relatively smallamount of distraction can cause the threshold to be exceeded. If it isdetermined that the combination of the distraction score and the focusscore is greater than the threshold, (512:YES), then display manager 430proceeds to operation 514. If it is determined that the combination ofthe distraction score and the focus score is not greater (less than)than the threshold, (512:NO), then display manager 430 returns tooperation 508 to identify a new distraction.

At operation 514, display manager 430 obscures the distraction. In someembodiments, the obscuring is performed by AR interface 424. In someembodiments, AR interface 424 can project an image to obscure the viewof the distraction. In some embodiments, operation 514 can includechanging an automation level. This can occur when obscuring in notfeasible (e.g., obscure windshield). Increasing the automation level candecrease the focus score such that the driver is not distracted fromtasks to perform. In response to completing operation 514, displaymanager 430 returns to operation 508 to identify a new potentialdistraction.

In some embodiments, display manager 430 continuously monitors forchanges to a driver, a driving mode, and/or the focus score. In responseto identifying a change in at least one of these factors, displaymanager 430 returns to the appropriate operation. For example, if adriver travels from an area that is familiar to them, to an unfamiliararea that can change the focus score, and display manager 430 can returnto operation 506 and procced with the remainder of method 500. This cancontinue until the car is no longer driving.

FIG. 6 depicts a flowchart of an example method, method 600, forselecting media to display below a distraction threshold that can beperformed in a computing environment (e.g., computing environment 400and/or cloud computing environment 50). One or more of the advantagesand improvements described above for selecting media to display below afocus threshold may be realized by method 600, consistent with variousembodiments of the present disclosure.

Method 600 can be implemented by one or more processors, host 410,vehicle 420, driving application 422, AR interface 424, sensors 426,display manager 430, driver profile 432, historical data 434, and/or adifferent combination of hardware and/or software. In variousembodiments, the various operations of method 500 are performed by oneor more of host 410, vehicle 420, driving application 422, AR interface424, sensors 426, display manager 430, driver profile 432, andhistorical data 434. For illustrative purposes, the method 600 will bedescribed as being performed by display manager 430.

At operation 602, display manager 430 identifies a driver. At operation604, display manager 430 determines a driving mode of the vehicle. AToperation 606, display manager 430 generates/calculates a focus score.In some embodiments, operations 602, 604, and 606 can be consistent withoperation 502, 504, and 506 of method 500.

At operation 608, display manager 430 selects media to display. In someembodiments, the media can be an advertisement. In some embodiments, themedia is requested and obtained from advertisement repository 412. Theselection can be configured to maximize the distraction score such thatthe focus score and the distraction score do not exceed the threshold(or stay below the threshold). The lower the focus score, the morecomplex the media can be. In some embodiments, the selected media can bebased on information in driver profile 432 and/or historical data 434.

In some embodiments, operation 608 includes selecting a time to displaythe media. The time can be based on a trigger. In some embodiments,operation 608 is in response to a trigger being activated. The triggercan be based on a location (e.g., predetermined locations), a time(e.g., time between displays), distance traveled, and the like.

At operation 610, display manager 430 displays the selected media. Insome embodiments, the media is an advertisement. The media can bedisplayed by AR interface 424 on any suitable surface. In someembodiments, operation 610 includes storing relevant information inhistorical data 434 and/or driver profile 432. The relevant informationcan be related to the current focus and distraction scores, interactionwith the displayed media, and other similar information. This can beused to generate future scores.

Computer Technology and Computer Readable Media

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user’s computer, partly on the user’s computer, as astandalone software package, partly on the user’s computer and partly ona remote computer or entirely on the remote computer or server. In thelatter scenario, the remote computer may be connected to the user’scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus, or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:identifying a driver of a vehicle, wherein the vehicle includes anaugmented reality (AR) system, and the vehicle is at least partiallyautomated; determining a driving mode for the vehicle; calculating afocus score, wherein the focus score is based on the driving mode;requesting, from a media repository, a media file, wherein the mediarepository contains a plurality of media files and each media file hasan associated distraction score; receiving from the media repository, afirst media file to display, wherein a first distraction score of thefirst media file combined with the focus score is below a safetythreshold; and displaying the first media file by the AR system.
 2. Thecomputer-implemented method of claim 1, wherein the driving mode iscorrelated to a minimum focus score.
 3. The computer-implemented methodof claim 2, wherein the focus score is further based on an experiencelevel of the driver.
 4. The computer-implemented method of claim 2,wherein the focus score is further based on environmental conditionssurrounding the vehicle.
 5. The computer-implemented method of claim 1,wherein the requesting is in response to an activation of a trigger. 6.The computer-implemented method of claim 1, wherein the requesting themedia file include the focus score, and a maximum distraction score. 7.The computer-implemented method of claim 1, wherein the driving mode isselected from a predetermined set of driving modes.
 8. Thecomputer-implemented method of claim 7, wherein each driving mode of theset of driving modes is assigned to one level of a set of levels, andeach level includes all functionality of a lower level with at least oneadditional automated feature of the vehicle.
 9. The computer-implementedmethod of claim 1, wherein the focus score is a representation of aminimum amount of attention required of the driver for driving thevehicle.
 10. The computer-implemented method of claim 1, wherein thefirst media file is an advertisement.
 11. A system comprising: aprocessor; and a computer-readable storage medium communicativelycoupled to the processor and storing program instructions which, whenexecuted by the processor, are configured to cause the processor to:identify a driver of a vehicle, wherein the vehicle includes anaugmented reality (AR) system, and the vehicle is at least partiallyautomated; determine, a driving mode for the vehicle; calculate a focusscore, wherein the focus score is based on the driving mode; identify afirst distraction along a route the vehicle is traveling; calculate adistraction score for the first distraction, wherein the distractionscore is based on a complexity of the first distraction; determine a sumof the focus score and the distraction score exceed a safety threshold;and reduce a risk of the first distraction.
 12. The system of claim 11,wherein the reduction of the risk include obscuring, by the AR system,the first distraction.
 13. The system of claim 12, wherein the obscuringincludes projecting an image on a window of the vehicle to obscure thefirst distraction.
 14. The system of claim 11, wherein the reduction ofthe risk includes changing the driving mode of the vehicle.
 15. Thesystem of claim 14, wherein the driving mode is one of a set of drivingmodes and each driving mode of the set of driving modes is assigned toone level of a set of levels, and each level includes all functionalityof a lower level with at least one additional automated feature of thevehicle.
 16. The system of claim 15, wherein the change of the drivingmode is to a higher level of the set of levels.
 17. The system of claim16, wherein the change of the driving mode causes a reduction in thefocus score, such that the sum of the focus score and the distractionscore decrease below the safety threshold.
 18. A computer programproduct, the computer program product comprising a computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by a processing unit to cause theprocessing unit to: identify a driver of a vehicle, wherein the vehicleincludes an augmented reality (AR) system, and the vehicle is at leastpartially automated; determine, a driving mode for the vehicle;calculate a focus score, wherein the focus score is based on the drivingmode; request, from a media repository, a media file, wherein the mediarepository contains a plurality of media files, and each media file hasan associated distraction score; receive from the media repository, afirst media file to display, wherein a first distraction score of thefirst media file combined with the focus score is below a safetythreshold; and display the first media file by the AR system.
 19. Thecomputer program product of claim 18, wherein the request for the mediafile includes the focus score, and a maximum distraction score.
 20. Thecomputer program product of claim 19, wherein the focus score is furtherbased on an experience level of the driver, the focus score is furtherbased on environmental conditions surrounding the vehicle, and themaximum distraction score represents a complexity of the first mediafile.